5 Widely used Tools and Techniques for Text Analytics

Text analytics is an advanced analytics technique that helps in the extraction of structured data of supreme quality from the unstructured text.

It is referred to as text mining. One of the prominent reasons owing to which people use it is for the extraction of additional data from the unstructured data sources with an eye to enriching the master data of the customers with an eye to production the new customer insight.

It is also useful for the determination of sentiments and different types of products and services.

Results of the survey, tweets, online reviews, emails and different kinds of written feedback consist of insight into the customers.

The recorded interactions have a bunch of information that can be transformed into the text without any hassles.

According to Markets and Markets, the global text analytics market value is forecast to reach USD 8.8 billion by 2022 with a compound annual growth rate (CAGR) of 17.2 percent. Social media analytics and the growing need for predictive analytics for businesses are among the major factors driving the growth of the text analytics market.

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With the aid of text analytics, you will be capable of uncovering a wide array of themes and patterns.

Thus, you will have an information about the thoughts of your customers. With it, you will be able to gain an understanding of their requirements and needs.

Top Tools for Text analytics

Text analytics with Hadoop

Analyzing text with the aid of Hadoop happens to be an amazing option when the full volume of the source files is huge and the Hadoop Cluster has prerequisite sources.

Thus, the analysis of the text happens more quickly in Hadoop. The text analysis is known for the extraction of entities from the unstructured text.

It is helpful for the transformation of the unstructured data into the structured data. This is crucial for running any sort of analysis with the aid of the data in text resources.

You just require the text sources as the input for the analysis of the text.

Later on, you can remove the text sources theoretically, as they will not be required for the process of analysis.

Text analytics with HANA

With the aid of SAP HANA, it is possible to extract real insight from the unstructured data.

This platform stands out of the ordinary in offering text analysis, search and text mining functionality from the unstructured text sources.

Statistical algorithms can be applied by which you can detect the patterns in the large document collections, which is inclusive of key term identification as well as document.

Almost 80 percent of the relevant information of the enterprise is derived from the unstructured data.

With the aid of SAP HANA, you can get access to the greater volume of data that is inclusive of unstructured text data from a wide array of sources. SAP HANA allows people to do the full-text analysis.

Text analytics with R

Here is the list of the leading four options that are used in the Big Data Services industry with an eye to accomplishing text analysis in R:

Keyword Match Algorithm

It is considered to be the most powerful tool for performing text analysis. It stands second to none in the extraction of keywords from the not so well separated keywords.

It comes with the option to assign priority to the algorithm. You, however, require a pre-defined list of keywords from where you require searching.

At times, it has been seen to capture a few types of misclassified cases.

Word match algorithm

This is known to be the fix for the min-classified cases that are found in the last algorithm.

Here, words are matched in lieu of the keywords. It functions in a perfect manner to find the well-separated words.

For example, with the aid of this algorithm, it is possible to extract the word Ramesh from Ramesh Shastri.

It enables the priority order as well. For example, in case you intend to give higher priority to Ramesh than Shastri in the above-mentioned tag, it can be executed at ease.

General Expressions

This process requires extensive research from the sentence structures. In order to begin with it, you do not require any sort of list.

The percentage of accuracy is really high if you gain success in finding the stronger and regular expression.

An in-depth research is required for the creation of regular expression. In case the data is not structured well, this process lets you tag a smaller number of the observations.

This algorithm can be used if you are not aware of the language of the text. It functions as the feedback to the other algorithm.

If parameters are optimized in a perfect manner, it can be predicted with ease. You do not need any dictionary.

It is also used for providing feedback to the other algorithms. At times, it is not that precise in the name of the subject.

It has a tendency for capturing the trends that do not indicate anything significant.

Text analytics with Excel

Excel is recognized to be an effective and convenient solution for accomplishing your requirements for text analysis.

You can go for an analysis of several customer reviews for gaining an insight into the product.

The Excel add-in functions on ParallelDots AI APIs that are used by the enterprises and developers for empowering the analytics for the past two years.

You can conduct keyword analysis on a bunch of negative and positive sentences with an eye to understanding why people are disliking or liking the product.

This analysis let you get an insight into the key phrases that contribute to the sentiment about the product.

For instance, a phone manufacturer can conduct the analysis of reviews from the social media, eCommerce sites, and tech review blogs.

After that, keywords can be extracted for the negative and positive sentiment sentences for finding the features, disliked or liked by the users regarding about a specific phone model.

In addition to this, you can go to a higher level by the analysis of the product reviews and then categorize the same with an eye to identifying if the review is a query, feedback, spam or opinion.

This is useful to filter the essential reviews and then act on them quickly.

You can correlate the analyzed data such as intent, keywords, and sentiment with the internal business metrics like sales data, marketing spends for getting actionable insights.

Text analytics with Python

Here are some of the applications in which python stays at the forefront that enable the use of a wide assortment of advanced libraries, specifically the natural language processing toolkit.

It comprises of a series of libraries and advanced functions for the performance of specific operation present in the text for pre-processing it for using the same for the derivation of information from the same.


Though customer service is present for most of the products, it is not available always effective as most of the people want their complaints should be solved during the usual working hours, thereby resulting in a rush. Chatbots are considered the perfect solution to the same.

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Sentimental analysis

During online shopping, most of the customers provide feedback. This feedback is classified into the categories like negative, positive and neutral, thereby letting the customers make a better-purchased decision about the product.

It also helps the company in filtering out the flaws from the negative reviews for the improvement of the product.


The text analytics is known for conferring the early warning of the trouble as it showcases the points, your clients are not satisfied with.

With the aid of the text analytics tool, you will gain success in extracting valuable details from the data that cannot be quantified in the other ways at ease.

It is useful in turning the unstructured thoughts of customers into the unstructured data at ease that you can use for your business.

Author: Pradeep Parthiban
Pradeep is a Content Writer and Digital Marketing Specialist at Indium Software with a demonstrated history of working in the information technology and services industry.